The **Logistic Regression** block enables you to apply a logistic regression predictive model to a dataset.

The following demonstrates how to use the **Logistic Regression** block to create a logistic regression model for an input dataset *ExamResults.csv* (which contains observations that describe a range of test scores from a schoo) that predicts the likelihood of a student passing an exam based on their hours of study.

- Import the
*ExamResults.csv*dataset onto a Workflow canvas using the**Text File Import**block. - Expand the
**Model Training**group in the Workflow palette, then click and drag a**Logistic Regression**block onto the Workflow canvas. - Click the Output port of the
*ExamResults*dataset block and drag a connection towards the**Input**port of the**Logistic Regression**block. - Double-click the
**Logistic Regression**block to display the**Configure Logistic Regression**dialog box. - In the
**Logistic Regression**dialog box:

- In the
**Dependent variable**drop-down list, select**Pass?**. - From the
**Event**drop-down list, select**1**(one). - In the
**Unselected Effect Variables**list, select**HoursStudy**. - Click
**Select**to move the variable to the**Selected Effect Variables**list. - Clear the
**Class**checkbox for the variable.

- In the
- Click
**OK**to save the configuration and close the**Configure Logistic Regression**dialog box.

A green execution status is displayed in the **Output** ports of the **Logistic Regression **block and the new **Logistic Regression Model**. The **Logistic Regression** block output can be used with a **Score** block to make predictions on a dataset.